9 of 9 Metaflow Jobs

PySpark and Spark SQL - Expert knowledge of ML Ops frameworks in the following categories: a) experiment tracking and model metadata management (e.g. MLflow) b) orchestration of ML workflows (e.g. Metaflow) c) data and pipeline versioning (e.g. Data Version Control) d) model deployment, serving and monitoring (e.g. Kubeflow) - Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines More ❯

Numpy, Pandas, PySpark and Spark SQL Expert knowledge of ML Ops frameworks in the following categories: experiment tracking and model metadata management (e.g. MLflow) orchestration of ML workflows (e.g. Metaflow) data and pipeline versioning (e.g. Data Version Control) model deployment, serving and monitoring (e.g. Kubeflow) Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform More ❯

Numpy, Pandas, PySpark and Spark SQL Expert knowledge of ML Ops frameworks in the following categories: experiment tracking and model metadata management (e.g. MLflow) orchestration of ML workflows (e.g. Metaflow) data and pipeline versioning (e.g. Data Version Control) model deployment, serving and monitoring (e.g. Kubeflow) Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform More ❯

Numpy, Pandas, PySpark and Spark SQL Expert knowledge of ML Ops frameworks in the following categories: experiment tracking and model metadata management (e.g. MLflow) orchestration of ML workflows (e.g. Metaflow) data and pipeline versioning (e.g. Data Version Control) model deployment, serving and monitoring (e.g. Kubeflow) Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform More ❯

Numpy, Pandas, PySpark and Spark SQL Expert knowledge of ML Ops frameworks in the following categories: experiment tracking and model metadata management (e.g. MLflow) orchestration of ML workflows (e.g. Metaflow) data and pipeline versioning (e.g. Data Version Control) model deployment, serving and monitoring (e.g. Kubeflow) Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform More ❯

Python and ML/engineering frameworks such as PyTorch, TensorFlow (including Keras), Hugging Face (Transformers, Datasets) and scikit-learn, etc Experience with MLOps tools, including MLFlow, workflow orchestrators (Airflow, Metaflow, Perfect or similar), and containerisation (Docker) Strong knowledge of cloud platforms like Azure, AWS or GCP for deploying and managing ML models Familiarity with data engineering tools and practices, e.g. More ❯

/testing datasets, cross-validation, performance visualisation, and use hosted APIs; explore techniques like time-series forecasting, clustering, or Bayesian inference. Orchestration and Parallelisation : Manage workflows with tools like Metaflow, MLFlow, AirFlow, or DVC; utilise parallelisation frameworks like PySpark or Ray for efficient model processing. Exposure to cloud platforms (AWS, Azure, GCP) Why you should join us Artefact is revolutionizing More ❯

difference – and who brings the following: Strong Python coding skills. Proficiency with Azure and orchestration tools like Azure DevOps (ADO). Experience with MLOps frameworks such as MLflow, Kubeflow, Metaflow, or SageMaker. Experience deploying real-time inference services and batch prediction pipelines. Solid understanding of CI/CD practices and monitoring tools (e.g., Prometheus, Grafana). Benefits Our benefits are More ❯

datasets suitable for machine learning research and production. High-Performance Data Pipelines Develop and optimize distributed systems for data processing, including filtering, indexing, and retrieval, leveraging frameworks like Ray, Metaflow, Spark, or Hadoop. Synthetic Data Generation Build and orchestrate pipelines to generate synthetic data at scale, advancing research on cost-efficient inference and training strategies. Experiments & Analysis Design and conduct More ❯